Overview

Dataset statistics

Number of variables14
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.8 KiB
Average record size in memory163.5 B

Variable types

Numeric11
Categorical2
DateTime1

Alerts

BUI is highly overall correlated with Classes and 7 other fieldsHigh correlation
Classes is highly overall correlated with BUI and 7 other fieldsHigh correlation
DC is highly overall correlated with BUI and 7 other fieldsHigh correlation
DMC is highly overall correlated with BUI and 8 other fieldsHigh correlation
FFMC is highly overall correlated with BUI and 8 other fieldsHigh correlation
FWI is highly overall correlated with BUI and 8 other fieldsHigh correlation
ISI is highly overall correlated with BUI and 8 other fieldsHigh correlation
RH is highly overall correlated with DMC and 4 other fieldsHigh correlation
Rain is highly overall correlated with BUI and 6 other fieldsHigh correlation
Region is highly overall correlated with Unnamed: 0High correlation
Temperature is highly overall correlated with BUI and 7 other fieldsHigh correlation
Unnamed: 0 is highly overall correlated with RegionHigh correlation
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Rain has 133 (54.7%) zerosZeros
ISI has 4 (1.6%) zerosZeros
FWI has 9 (3.7%) zerosZeros

Reproduction

Analysis started2024-03-21 10:36:43.990234
Analysis finished2024-03-21 10:36:58.441520
Duration14.45 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct243
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121
Minimum0
Maximum242
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:36:58.544687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.1
Q160.5
median121
Q3181.5
95-th percentile229.9
Maximum242
Range242
Interquartile range (IQR)121

Descriptive statistics

Standard deviation70.292247
Coefficient of variation (CV)0.58092766
Kurtosis-1.2
Mean121
Median Absolute Deviation (MAD)61
Skewness0
Sum29403
Variance4941
MonotonicityStrictly increasing
2024-03-21T10:36:58.726552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.4%
182 1
 
0.4%
154 1
 
0.4%
155 1
 
0.4%
156 1
 
0.4%
157 1
 
0.4%
158 1
 
0.4%
159 1
 
0.4%
160 1
 
0.4%
161 1
 
0.4%
Other values (233) 233
95.9%
ValueCountFrequency (%)
0 1
0.4%
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
ValueCountFrequency (%)
242 1
0.4%
241 1
0.4%
240 1
0.4%
239 1
0.4%
238 1
0.4%
237 1
0.4%
236 1
0.4%
235 1
0.4%
234 1
0.4%
233 1
0.4%

Temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.152263
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:36:58.877317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.9
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6280395
Coefficient of variation (CV)0.11283932
Kurtosis-0.14141446
Mean32.152263
Median Absolute Deviation (MAD)3
Skewness-0.19132733
Sum7813
Variance13.16267
MonotonicityNot monotonic
2024-03-21T10:36:59.041260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
35 29
11.9%
31 25
10.3%
34 24
9.9%
33 23
9.5%
30 22
9.1%
36 21
8.6%
32 21
8.6%
29 18
7.4%
28 15
6.2%
37 8
 
3.3%
Other values (9) 37
15.2%
ValueCountFrequency (%)
22 2
 
0.8%
24 3
 
1.2%
25 6
 
2.5%
26 5
 
2.1%
27 8
 
3.3%
28 15
6.2%
29 18
7.4%
30 22
9.1%
31 25
10.3%
32 21
8.6%
ValueCountFrequency (%)
42 1
 
0.4%
40 3
 
1.2%
39 6
 
2.5%
38 3
 
1.2%
37 8
 
3.3%
36 21
8.6%
35 29
11.9%
34 24
9.9%
33 23
9.5%
32 21
8.6%

RH
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.041152
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:36:59.213318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152.5
median63
Q373.5
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.82816
Coefficient of variation (CV)0.23900523
Kurtosis-0.50894281
Mean62.041152
Median Absolute Deviation (MAD)11
Skewness-0.24279046
Sum15076
Variance219.87433
MonotonicityNot monotonic
2024-03-21T10:36:59.399829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 10
 
4.1%
64 10
 
4.1%
78 8
 
3.3%
54 8
 
3.3%
58 8
 
3.3%
73 7
 
2.9%
80 7
 
2.9%
66 7
 
2.9%
65 7
 
2.9%
68 7
 
2.9%
Other values (52) 164
67.5%
ValueCountFrequency (%)
21 1
 
0.4%
24 1
 
0.4%
26 1
 
0.4%
29 1
 
0.4%
31 1
 
0.4%
33 2
0.8%
34 3
1.2%
35 1
 
0.4%
36 1
 
0.4%
37 3
1.2%
ValueCountFrequency (%)
90 1
 
0.4%
89 3
1.2%
88 3
1.2%
87 4
1.6%
86 3
1.2%
84 2
 
0.8%
83 1
 
0.4%
82 3
1.2%
81 6
2.5%
80 7
2.9%

Ws
Real number (ℝ)

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.493827
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:36:59.564373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8113853
Coefficient of variation (CV)0.18145196
Kurtosis2.6217035
Mean15.493827
Median Absolute Deviation (MAD)2
Skewness0.55558584
Sum3765
Variance7.9038874
MonotonicityNot monotonic
2024-03-21T10:36:59.721190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
14 43
17.7%
15 40
16.5%
13 30
12.3%
17 28
11.5%
16 27
11.1%
18 25
10.3%
19 15
 
6.2%
21 8
 
3.3%
12 7
 
2.9%
11 7
 
2.9%
Other values (8) 13
 
5.3%
ValueCountFrequency (%)
6 1
 
0.4%
8 1
 
0.4%
9 2
 
0.8%
10 3
 
1.2%
11 7
 
2.9%
12 7
 
2.9%
13 30
12.3%
14 43
17.7%
15 40
16.5%
16 27
11.1%
ValueCountFrequency (%)
29 1
 
0.4%
26 1
 
0.4%
22 2
 
0.8%
21 8
 
3.3%
20 2
 
0.8%
19 15
 
6.2%
18 25
10.3%
17 28
11.5%
16 27
11.1%
15 40
16.5%

Rain
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76296296
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:36:59.882123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.37
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.0032068
Coefficient of variation (CV)2.6255623
Kurtosis25.822987
Mean0.76296296
Median Absolute Deviation (MAD)0
Skewness4.5686298
Sum185.4
Variance4.0128375
MonotonicityNot monotonic
2024-03-21T10:37:00.059458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.5 5
 
2.1%
1.2 3
 
1.2%
1.1 3
 
1.2%
Other values (29) 40
 
16.5%
ValueCountFrequency (%)
0 133
54.7%
0.1 18
 
7.4%
0.2 11
 
4.5%
0.3 10
 
4.1%
0.4 8
 
3.3%
0.5 5
 
2.1%
0.6 6
 
2.5%
0.7 6
 
2.5%
0.8 2
 
0.8%
0.9 1
 
0.4%
ValueCountFrequency (%)
16.8 1
0.4%
13.1 1
0.4%
10.1 1
0.4%
8.7 1
0.4%
8.3 1
0.4%
7.2 1
0.4%
6.5 1
0.4%
6 1
0.4%
5.8 1
0.4%
4.7 1
0.4%

FFMC
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.842387
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:37:00.233156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.13
Q171.85
median83.3
Q388.3
95-th percentile92.19
Maximum96
Range67.4
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation14.349641
Coefficient of variation (CV)0.18434226
Kurtosis1.040087
Mean77.842387
Median Absolute Deviation (MAD)5.8
Skewness-1.3201301
Sum18915.7
Variance205.9122
MonotonicityNot monotonic
2024-03-21T10:37:00.412552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.9 7
 
2.9%
89.4 5
 
2.1%
89.1 4
 
1.6%
85.4 4
 
1.6%
89.3 4
 
1.6%
88.3 3
 
1.2%
78.3 3
 
1.2%
87 3
 
1.2%
88.1 3
 
1.2%
47.4 3
 
1.2%
Other values (163) 204
84.0%
ValueCountFrequency (%)
28.6 1
0.4%
30.5 1
0.4%
36.1 1
0.4%
37.3 1
0.4%
37.9 1
0.4%
40.9 1
0.4%
41.1 1
0.4%
42.6 1
0.4%
44.9 1
0.4%
45 1
0.4%
ValueCountFrequency (%)
96 1
0.4%
94.3 1
0.4%
94.2 1
0.4%
93.9 2
0.8%
93.8 1
0.4%
93.7 1
0.4%
93.3 1
0.4%
93 1
0.4%
92.5 2
0.8%
92.2 2
0.8%

DMC
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.680658
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:37:00.593657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.8
95-th percentile41.04
Maximum65.9
Range65.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.39304
Coefficient of variation (CV)0.84417465
Kurtosis2.462551
Mean14.680658
Median Absolute Deviation (MAD)6.9
Skewness1.5229829
Sum3567.4
Variance153.58743
MonotonicityNot monotonic
2024-03-21T10:37:00.768373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 5
 
2.1%
1.9 4
 
1.6%
12.5 4
 
1.6%
16 3
 
1.2%
7 3
 
1.2%
2.5 3
 
1.2%
9.7 3
 
1.2%
3.2 3
 
1.2%
1.3 3
 
1.2%
2.6 3
 
1.2%
Other values (155) 209
86.0%
ValueCountFrequency (%)
0.7 1
 
0.4%
0.9 2
0.8%
1.1 2
0.8%
1.2 1
 
0.4%
1.3 3
1.2%
1.7 1
 
0.4%
1.9 4
1.6%
2.1 1
 
0.4%
2.2 2
0.8%
2.4 1
 
0.4%
ValueCountFrequency (%)
65.9 1
0.4%
61.3 1
0.4%
56.3 1
0.4%
54.2 1
0.4%
51.3 1
0.4%
50.2 1
0.4%
47 1
0.4%
46.6 1
0.4%
46.1 1
0.4%
45.6 1
0.4%

DC
Real number (ℝ)

HIGH CORRELATION 

Distinct197
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.430864
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:37:00.956773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q112.35
median33.1
Q369.1
95-th percentile158.94
Maximum220.4
Range213.5
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation47.665606
Coefficient of variation (CV)0.96428834
Kurtosis1.5964668
Mean49.430864
Median Absolute Deviation (MAD)23.9
Skewness1.4734602
Sum12011.7
Variance2272.01
MonotonicityNot monotonic
2024-03-21T10:37:01.137218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 5
 
2.1%
8.4 4
 
1.6%
7.8 4
 
1.6%
7.5 4
 
1.6%
8.3 4
 
1.6%
8.2 4
 
1.6%
7.6 4
 
1.6%
17 3
 
1.2%
15.2 2
 
0.8%
10 2
 
0.8%
Other values (187) 207
85.2%
ValueCountFrequency (%)
6.9 1
 
0.4%
7 2
0.8%
7.1 1
 
0.4%
7.3 2
0.8%
7.4 2
0.8%
7.5 4
1.6%
7.6 4
1.6%
7.7 2
0.8%
7.8 4
1.6%
7.9 1
 
0.4%
ValueCountFrequency (%)
220.4 1
0.4%
210.4 1
0.4%
200.2 1
0.4%
190.6 1
0.4%
181.3 1
0.4%
180.4 1
0.4%
177.3 1
0.4%
171.3 1
0.4%
168.2 1
0.4%
167.2 1
0.4%

ISI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7423868
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:37:01.310727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.25
95-th percentile13.38
Maximum19
Range19
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.1542338
Coefficient of variation (CV)0.87597954
Kurtosis0.86232522
Mean4.7423868
Median Absolute Deviation (MAD)2.4
Skewness1.1402426
Sum1152.4
Variance17.257659
MonotonicityNot monotonic
2024-03-21T10:37:01.615451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 8
 
3.3%
1.2 7
 
2.9%
0.4 5
 
2.1%
5.2 5
 
2.1%
4.7 5
 
2.1%
2.8 5
 
2.1%
1 5
 
2.1%
1.5 5
 
2.1%
5.6 5
 
2.1%
0.1 4
 
1.6%
Other values (96) 189
77.8%
ValueCountFrequency (%)
0 4
1.6%
0.1 4
1.6%
0.2 4
1.6%
0.3 3
1.2%
0.4 5
2.1%
0.5 2
 
0.8%
0.6 4
1.6%
0.7 4
1.6%
0.8 3
1.2%
0.9 2
 
0.8%
ValueCountFrequency (%)
19 1
0.4%
18.5 1
0.4%
17.2 1
0.4%
16.6 1
0.4%
16 1
0.4%
15.7 2
0.8%
15.5 1
0.4%
14.3 1
0.4%
14.2 1
0.4%
13.8 2
0.8%

BUI
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.690535
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:37:01.790959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.42
Q16
median12.4
Q322.65
95-th percentile46.4
Maximum68
Range66.9
Interquartile range (IQR)16.65

Descriptive statistics

Standard deviation14.228421
Coefficient of variation (CV)0.85248443
Kurtosis1.9560166
Mean16.690535
Median Absolute Deviation (MAD)7.3
Skewness1.4527448
Sum4055.8
Variance202.44797
MonotonicityNot monotonic
2024-03-21T10:37:01.965653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
2.1%
5.1 4
 
1.6%
3.9 3
 
1.2%
2.4 3
 
1.2%
8.3 3
 
1.2%
4.4 3
 
1.2%
2.9 3
 
1.2%
22.4 3
 
1.2%
11.5 3
 
1.2%
14.1 3
 
1.2%
Other values (163) 210
86.4%
ValueCountFrequency (%)
1.1 1
 
0.4%
1.4 2
0.8%
1.6 2
0.8%
1.7 2
0.8%
1.8 2
0.8%
2.2 1
 
0.4%
2.4 3
1.2%
2.6 2
0.8%
2.7 2
0.8%
2.8 2
0.8%
ValueCountFrequency (%)
68 1
0.4%
67.4 1
0.4%
64 1
0.4%
62.9 1
0.4%
59.5 1
0.4%
59.3 1
0.4%
57.1 1
0.4%
54.9 1
0.4%
54.7 1
0.4%
50.9 1
0.4%

FWI
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0353909
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-21T10:37:02.140175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.2
Q311.45
95-th percentile21.53
Maximum31.1
Range31.1
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.4405677
Coefficient of variation (CV)1.0575912
Kurtosis0.65498526
Mean7.0353909
Median Absolute Deviation (MAD)3.8
Skewness1.1475925
Sum1709.6
Variance55.362048
MonotonicityNot monotonic
2024-03-21T10:37:02.315608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 12
 
4.9%
0.8 10
 
4.1%
0.5 9
 
3.7%
0.1 9
 
3.7%
0 9
 
3.7%
0.3 8
 
3.3%
0.9 7
 
2.9%
0.2 6
 
2.5%
0.7 5
 
2.1%
0.6 4
 
1.6%
Other values (115) 164
67.5%
ValueCountFrequency (%)
0 9
3.7%
0.1 9
3.7%
0.2 6
2.5%
0.3 8
3.3%
0.4 12
4.9%
0.5 9
3.7%
0.6 4
 
1.6%
0.7 5
2.1%
0.8 10
4.1%
0.9 7
2.9%
ValueCountFrequency (%)
31.1 1
0.4%
30.3 1
0.4%
30.2 1
0.4%
30 1
0.4%
26.9 1
0.4%
26.3 1
0.4%
26.1 1
0.4%
25.4 1
0.4%
24.5 1
0.4%
24 1
0.4%

Classes
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
1
137 
0
106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Length

2024-03-21T10:37:02.475202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T10:37:02.589562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring characters

ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 137
56.4%
0 106
43.6%

Region
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
0
163 
1
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Length

2024-03-21T10:37:02.711815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-21T10:37:02.823607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring characters

ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 163
67.1%
1 80
32.9%

date
Date

Distinct122
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum2012-06-01 00:00:00
Maximum2012-09-30 00:00:00
2024-03-21T10:37:02.970516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:37:03.187988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-03-21T10:36:56.729722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.246470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.603639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.901213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.113578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.259807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.599965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.786288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.959392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.193708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.468113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.829759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.349184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.720090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.004589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.218385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.364665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.698601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.884630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.060140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.296098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.604566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.948186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.468324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.844575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.123985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.340877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.482680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.817754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.003369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.179477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.415604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.732233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.057844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.604472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.969456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.233281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.453068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.710568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.927075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.114774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.284184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.535539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.847774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.159616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.707806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.081749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.335778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.548896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.812342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.027995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.217835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.380554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.643353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.954638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.274847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.822312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.203568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.448358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.658083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.922033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.140161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.325877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.483915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.760098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.071554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.383308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:44.940091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.319081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.555388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.760367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.030531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.244761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.430642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.585694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.872368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.181071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.492668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.048368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.430657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.662451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.859171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.141653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.350253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.533296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.684128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.982200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.292082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.591008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.163493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.534896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.760462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:48.949874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.241952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.448838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.628184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.772705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.084621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.393181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.705316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.281925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.664281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.877048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.054954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.363003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.564593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.737832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:53.991586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.196398image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.509205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:57.816562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:45.493639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:46.784298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:47.996320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:49.161609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:50.485782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:51.675525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:52.847786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:54.094623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:55.316532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:36:56.621000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-21T10:37:03.324531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
BUIClassesDCDMCFFMCFWIISIRHRainRegionTemperatureUnnamed: 0Ws
BUI1.0000.7260.9430.9880.8070.9110.811-0.467-0.5760.0000.5860.0260.027
Classes0.7261.0000.6730.7170.8560.8420.857-0.421-0.6830.0500.521-0.073-0.023
DC0.9430.6731.0000.8930.7350.8490.746-0.347-0.6120.0770.505-0.0020.060
DMC0.9880.7170.8931.0000.8220.9160.822-0.505-0.5590.1340.6110.0340.001
FFMC0.8070.8560.7350.8221.0000.9680.989-0.665-0.7410.0000.666-0.038-0.067
FWI0.9110.8420.8490.9160.9681.0000.975-0.598-0.7180.0870.657-0.0250.034
ISI0.8110.8570.7460.8220.9890.9751.000-0.643-0.7380.0000.648-0.0360.032
RH-0.467-0.421-0.347-0.505-0.665-0.598-0.6431.0000.1790.000-0.643-0.0270.201
Rain-0.576-0.683-0.612-0.559-0.741-0.718-0.7380.1791.0000.000-0.2930.0470.011
Region0.0000.0500.0770.1340.0000.0870.0000.0000.0001.0000.0590.814-0.003
Temperature0.5860.5210.5050.6110.6660.6570.648-0.643-0.2930.0591.0000.055-0.224
Unnamed: 00.026-0.073-0.0020.034-0.038-0.025-0.036-0.0270.0470.8140.0551.0000.030
Ws0.027-0.0230.0600.001-0.0670.0340.0320.2010.011-0.003-0.2240.0301.000

Missing values

2024-03-21T10:36:57.977348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:36:58.344801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0TemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegiondate
002777160.064.83.014.21.23.90.5002012-06-05
113354130.088.29.930.56.410.97.2102012-06-07
223073150.086.612.138.35.613.57.1102012-06-08
332879120.073.29.546.31.312.60.9002012-06-10
443078200.559.04.67.81.04.40.4002012-06-14
552880173.149.43.07.40.43.00.1002012-06-15
663089160.637.31.17.80.01.60.0002012-06-17
773155160.179.94.516.02.55.31.4002012-06-19
883262180.181.48.247.73.311.53.8102012-06-23
993164180.086.817.871.86.721.610.6102012-06-26
Unnamed: 0TemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesRegiondate
2332333337160.092.261.3167.213.164.030.3112012-08-26
2342343553170.580.220.7149.22.730.65.9112012-08-29
2352352867190.075.42.916.32.04.00.8012012-09-02
2362363066150.273.54.126.61.56.00.7012012-09-04
2372373073140.079.26.516.62.16.61.2012012-09-11
2382382881150.084.612.641.54.314.35.7112012-09-14
2392393444120.092.525.263.311.226.217.5112012-09-17
2402403458130.279.518.788.02.124.43.8012012-09-20
2412412870150.079.913.836.12.414.13.0012012-09-25
2422422787290.545.93.57.90.43.40.2012012-09-28